Machine learning is a form of artificial intelligence that is employed to allow computers to evolve behaviors based on empirical data. Machine learning may take advantage of training examples to capture characteristics of interest of their unknown underlying probability distribution. Training data may be seen as examples that illustrate relations between observed variables. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data.
One application for machine learning is lead response management (LRM). LRM is the process of responding to leads in a manner that optimizes a target behavior in the lead, such as a qualification or a sale. Leads may come from a variety of sources such as a list purchased from a lead vendor. When lead information comes into an organization, the output decision of how to respond to the lead may include multiple interdependent components such as, but not limited to, who should respond to the lead, what method should be employed to respond to the lead, what content should be included in the response message, and when should the response take place. Each of these components of the output decision depends on both the input (such as the lead information) and the other components. For example, the timing of the response may depend on the availability of the person selected to respond. Also, the content of the message may depend on the method of response (e.g., since the length of an email message is not limited like the length of a text message).
One main difficulty using machine learning in LRM involves identifying appropriate inputs for use in the machine learning that will result in accurate suggestions of how to respond to leads in a manner that optimizes a target behavior. For example, a number of different relevant variables may come into play that may affect the probability of a particular lead exhibiting a target behavior, such as being qualified or making a purchase, but identifying these relevant variables can be difficult. Failing to identify relevant variables can decrease the effectiveness of LRM decisions arrived at using machine learning.
The subject matter claimed herein is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one example technology area where some embodiments described herein may be practiced.